{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a5c1d92f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch.autograd.grad_mode.set_grad_enabled at 0x7f504fc1ed30>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "\n",
    "import pandas as pd\n",
    "import einops\n",
    "\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from fancy_einsum import einsum\n",
    "from tqdm import tqdm\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib.gridspec import GridSpec\n",
    "from transformer_lens import (\n",
    "    HookedTransformer,\n",
    ")\n",
    "from toxicity.figures.fig_utils import convert, load_hooked, get_svd\n",
    "from constants import MODEL_DIR, DATA_DIR\n",
    "\n",
    "torch.set_grad_enabled(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6d7bc3f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded pretrained model gpt2-medium into HookedTransformer\n",
      "Loaded pretrained model gpt2-medium into HookedTransformer\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = load_hooked(\n",
    "    \"gpt2-medium\",\n",
    "    os.path.join(MODEL_DIR, \"dpo.pt\"),\n",
    ")\n",
    "gpt2 = HookedTransformer.from_pretrained(\"gpt2-medium\")\n",
    "gpt2.tokenizer.padding_side = \"left\"\n",
    "gpt2.tokenizer.pad_token_id = gpt2.tokenizer.eos_token_id\n",
    "\n",
    "toxic_vector = torch.load(os.path.join(MODEL_DIR, \"probe.pt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8eb39b7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "with open(\n",
    "    os.path.join(DATA_DIR, \"intervene_data/challenge_prompts.jsonl\"), \"r\"\n",
    ") as file_p:\n",
    "    data = file_p.readlines()\n",
    "\n",
    "prompts = [json.loads(x.strip())[\"prompt\"] for x in data]\n",
    "tokenized_prompts = model.to_tokens(prompts, prepend_bos=True).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a634ef30",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "_, scores_gpt2 = get_svd(gpt2, toxic_vector, 128)\n",
    "vectors_of_interest = [\n",
    "    (_score_obj[2], _score_obj[1], _score_obj[0])\n",
    "    for _score_obj in scores_gpt2[:64]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9583a20e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grabbing mlp mids...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  8.18it/s]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "gpt2_resid = []\n",
    "dpo_resid = []\n",
    "sample_size = 50\n",
    "batch_size = 4\n",
    "print(\"Grabbing mlp mids...\")\n",
    "_vec = vectors_of_interest[0]\n",
    "_layer = _vec[0]\n",
    "_idx = _vec[1]\n",
    "\n",
    "for idx in tqdm(range(0, sample_size, batch_size)):\n",
    "    batch = tokenized_prompts[idx : idx + batch_size, :]\n",
    "    dpo_batch = batch.clone()\n",
    "\n",
    "    with torch.inference_mode():\n",
    "        _, cache = gpt2.run_with_cache(batch)\n",
    "        resid = cache[f\"blocks.{_layer}.hook_resid_mid\"][:, -1, :]\n",
    "\n",
    "    gpt2_resid.extend(resid.cpu().tolist())\n",
    "\n",
    "    with torch.inference_mode():\n",
    "        _, cache = model.run_with_cache(dpo_batch)\n",
    "        resid = cache[f\"blocks.{_layer}.hook_resid_mid\"][:, -1, :]\n",
    "    dpo_resid.extend(resid.cpu().tolist())\n",
    "\n",
    "\n",
    "w_ins = [\n",
    "    gpt2.blocks[_layer].mlp.W_in[:, _idx].cpu(),\n",
    "    model.blocks[_layer].mlp.W_in[:, _idx].cpu(),\n",
    "]\n",
    "\n",
    "gpt2_stacked = torch.stack([torch.Tensor(x) for x in gpt2_resid], dim=0)\n",
    "dpo_stacked = torch.stack([torch.Tensor(x) for x in dpo_resid], dim=0)\n",
    "gpt2_dots = einsum(\"sample d_model, d_model\", gpt2_stacked, w_ins[0])\n",
    "dpo_dots = einsum(\"sample d_model, d_model\", dpo_stacked, w_ins[1])\n",
    "gpt_acts = model.blocks[0].mlp.act_fn(gpt2_dots)\n",
    "dpo_acts = model.blocks[0].mlp.act_fn(dpo_dots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e2528de9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1024])\n",
      "11.933368682861328\n",
      "-0.0\n",
      "13.847200393676758\n",
      "-0.0\n",
      "26.401968002319336\n",
      "24.49401092529297\n",
      "5.835847854614258\n",
      "0.4391503632068634\n",
      "26.874244689941406\n",
      "10.650365829467773\n",
      "9.047333717346191\n",
      "1.1667826175689697\n",
      "12.976777076721191\n",
      "19.238019943237305\n",
      "-0.0\n",
      "-0.0\n",
      "26.568723678588867\n",
      "24.608760833740234\n",
      "8.549989700317383\n",
      "2.96732759475708\n",
      "12.697152137756348\n",
      "9.083166122436523\n",
      "-0.004181285854429007\n",
      "6.6472063064575195\n",
      "-0.0784885436296463\n",
      "18.135242462158203\n",
      "5.277504920959473\n",
      "0.8333429098129272\n",
      "14.884654998779297\n",
      "0.6342859268188477\n",
      "____\n",
      "1.7476341724395752\n",
      "-0.0\n",
      "4.84157657623291\n",
      "-0.0\n",
      "14.063610076904297\n",
      "12.575393676757812\n",
      "-0.1570473462343216\n",
      "-0.0\n",
      "12.956071853637695\n",
      "0.9889507293701172\n",
      "-0.1555277705192566\n",
      "-0.0\n",
      "2.1770577430725098\n",
      "5.681166172027588\n",
      "-0.0\n",
      "-0.0\n",
      "11.87281322479248\n",
      "10.284687042236328\n",
      "-0.0002197946305386722\n",
      "-3.951676262659021e-05\n",
      "3.119166135787964\n",
      "1.0556527376174927\n",
      "-0.0\n",
      "-0.16180898249149323\n",
      "-0.0\n",
      "7.465394020080566\n",
      "-0.01901228539645672\n",
      "-4.5808523282175884e-05\n",
      "2.4970977306365967\n",
      "-0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 430.125x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "all_data = torch.concat([gpt2_stacked, dpo_stacked], dim=0)\n",
    "mean = all_data.mean(dim=0)\n",
    "stddev = all_data.std(dim=0)\n",
    "normalized = (all_data - mean) / stddev\n",
    "\n",
    "U, S, V = torch.pca_lowrank(normalized)\n",
    "\n",
    "diff = dpo_stacked - gpt2_stacked\n",
    "diff_mean = diff.mean(dim=0)\n",
    "print(diff_mean.shape)\n",
    "\n",
    "comps = torch.concat([diff_mean.unsqueeze(-1), V], dim=1)\n",
    "comps = comps[:, :2]\n",
    "projected = torch.mm(normalized, comps)\n",
    "\n",
    "pca_raw = []\n",
    "num_samples = 30\n",
    "for idx in range(num_samples):\n",
    "\n",
    "    _activation = gpt_acts[idx].item()\n",
    "    if _activation > 15:\n",
    "        act = \"High (> 15)\"\n",
    "    elif _activation > 0:\n",
    "        act = \"Low (> 0)\"\n",
    "    else:\n",
    "        act = \"None\"\n",
    "\n",
    "    print(_activation)\n",
    "    pca_raw.append(\n",
    "        {\n",
    "            \"Model\": \"GPT2\",\n",
    "            \"x\": projected[idx, 0].item(),\n",
    "            \"y\": projected[idx, 1].item(),\n",
    "            \"Activated\": act,\n",
    "        }\n",
    "    )\n",
    "\n",
    "_offset = len(gpt2_resid)\n",
    "print(\"____\")\n",
    "for idx in range(num_samples):\n",
    "    _activation = dpo_acts[idx].item()\n",
    "    if _activation > 15:\n",
    "        act = \"High (> 15)\"\n",
    "    elif _activation > 0:\n",
    "        act = \"Low (> 0)\"\n",
    "    else:\n",
    "        act = \"None\"\n",
    "    print(_activation)\n",
    "    pca_raw.append(\n",
    "        {\n",
    "            \"Model\": \"DPO\",\n",
    "            \"x\": projected[_offset + idx, 0].item(),\n",
    "            \"y\": projected[_offset + idx, 1].item(),\n",
    "            \"Activated\": act,\n",
    "        }\n",
    "    )\n",
    "\n",
    "pca_data = pd.DataFrame(pca_raw)\n",
    "sns.set_theme(context=\"paper\", style=\"ticks\", rc={\"lines.linewidth\": 1})\n",
    "\n",
    "fig = sns.relplot(\n",
    "    pca_data,\n",
    "    x=\"x\",\n",
    "    y=\"y\",\n",
    "    hue=\"Activated\",\n",
    "    palette={\"High (> 15)\": \"red\", \"Low (> 0)\": \"orange\", \"None\": \"green\"},\n",
    "    hue_order=[\"High (> 15)\", \"Low (> 0)\", \"None\"],\n",
    "    style=\"Model\",\n",
    "    markers={\"GPT2\": \"o\", \"DPO\": \"^\"},\n",
    "    height=2.5,\n",
    "    aspect=3.25 / 2.5,\n",
    "    s=60,\n",
    "    legend=\"full\",\n",
    ")\n",
    "\n",
    "fig.ax.set_xticks([])\n",
    "fig.ax.set_yticks([])\n",
    "fig.ax.xaxis.label.set_text(\"Shift Component\")\n",
    "fig.ax.yaxis.label.set_text(\"Principle Component\")\n",
    "fig.ax.xaxis.label.set_visible(True)\n",
    "fig.ax.yaxis.label.set_visible(True)\n",
    "\n",
    "_offset = len(gpt2_resid)\n",
    "for idx in range(num_samples):\n",
    "    gpt2_x = projected[idx, 0].item()\n",
    "    gpt2_y = projected[idx, 1].item()\n",
    "    dpo_x = projected[_offset + idx, 0].item()\n",
    "    dpo_y = projected[_offset + idx, 1].item()\n",
    "    fig.ax.plot(\n",
    "        [gpt2_x, dpo_x], [gpt2_y, dpo_y], color=\"black\", ls=\":\", zorder=0\n",
    "    )\n",
    "\n",
    "plt.savefig(f\"pca_layer{_layer}.pdf\", bbox_inches=\"tight\", dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1bf0fa0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grabbing mlp mids...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  8.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.736486434936523\n",
      "-0.1509389728307724\n",
      "6.283715724945068\n",
      "-1.500771276141677e-07\n",
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      "-0.14904452860355377\n",
      "-0.11065510660409927\n",
      "-0.0\n",
      "-0.0\n",
      "____\n",
      "10.42776870727539\n",
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      "4.996962547302246\n",
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      "-0.0014133399818092585\n",
      "-0.017505863681435585\n",
      "-0.0\n",
      "-0.0\n",
      "Grabbing mlp mids...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  9.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.914581298828125\n",
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      "5.247683525085449\n",
      "10.284181594848633\n",
      "____\n",
      "5.576327323913574\n",
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      "-0.0\n",
      "-0.0\n",
      "0.047237005084753036\n",
      "7.308445453643799\n",
      "Grabbing mlp mids...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  8.97it/s]\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
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      "-0.07945673167705536\n",
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      "0.027278488501906395\n",
      "____\n",
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 675x350 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig = plt.figure(figsize=(6.75, 3.5))\n",
    "gs = GridSpec(1, 3)\n",
    "boundaries = [\n",
    "    (0.1, 10),\n",
    "    (0.1, 10),\n",
    "    (0.1, 10),\n",
    "]\n",
    "\n",
    "for vec_idx in [1, 2, 3]:\n",
    "\n",
    "    gpt2_resid = []\n",
    "    dpo_resid = []\n",
    "    sample_size = 50\n",
    "    batch_size = 4\n",
    "    print(\"Grabbing mlp mids...\")\n",
    "    _vec = vectors_of_interest[vec_idx]\n",
    "    _layer = _vec[0]\n",
    "    _idx = _vec[1]\n",
    "\n",
    "    for idx in tqdm(range(0, sample_size, batch_size)):\n",
    "        batch = tokenized_prompts[idx : idx + batch_size, :]\n",
    "        dpo_batch = batch.clone()\n",
    "\n",
    "        with torch.inference_mode():\n",
    "            _, cache = gpt2.run_with_cache(batch)\n",
    "            resid = cache[f\"blocks.{_layer}.hook_resid_mid\"][:, -1, :]\n",
    "\n",
    "        gpt2_resid.extend(resid.cpu().tolist())\n",
    "\n",
    "        with torch.inference_mode():\n",
    "            _, cache = model.run_with_cache(dpo_batch)\n",
    "            resid = cache[f\"blocks.{_layer}.hook_resid_mid\"][:, -1, :]\n",
    "        dpo_resid.extend(resid.cpu().tolist())\n",
    "\n",
    "    w_ins = [\n",
    "        gpt2.blocks[_layer].mlp.W_in[:, _idx].cpu(),\n",
    "        model.blocks[_layer].mlp.W_in[:, _idx].cpu(),\n",
    "    ]\n",
    "\n",
    "    gpt2_stacked = torch.stack([torch.Tensor(x) for x in gpt2_resid], dim=0)\n",
    "    dpo_stacked = torch.stack([torch.Tensor(x) for x in dpo_resid], dim=0)\n",
    "    gpt2_dots = einsum(\"sample d_model, d_model\", gpt2_stacked, w_ins[0])\n",
    "    dpo_dots = einsum(\"sample d_model, d_model\", dpo_stacked, w_ins[1])\n",
    "    gpt_acts = model.blocks[0].mlp.act_fn(gpt2_dots)\n",
    "    dpo_acts = model.blocks[0].mlp.act_fn(dpo_dots)\n",
    "\n",
    "    all_data = torch.concat([gpt2_stacked, dpo_stacked], dim=0)\n",
    "    mean = all_data.mean(dim=0)\n",
    "    stddev = all_data.std(dim=0)\n",
    "    normalized = (all_data - mean) / stddev\n",
    "\n",
    "    U, S, V = torch.pca_lowrank(normalized)\n",
    "\n",
    "    diff = dpo_stacked - gpt2_stacked\n",
    "    diff_mean = diff.mean(dim=0)\n",
    "\n",
    "    comps = torch.concat([diff_mean.unsqueeze(-1), V], dim=1)\n",
    "    comps = comps[:, :2]\n",
    "    projected = torch.mm(normalized, comps)\n",
    "\n",
    "    pca_raw = []\n",
    "    num_samples = 30\n",
    "\n",
    "    _boundary = boundaries[vec_idx - 1]\n",
    "    for idx in range(num_samples):\n",
    "\n",
    "        _activation = gpt_acts[idx].item()\n",
    "        if _activation > _boundary[1]:\n",
    "            act = f\"High (> {_boundary[1]})\"\n",
    "        elif _activation > _boundary[0]:\n",
    "            act = f\"Low (> {_boundary[0]})\"\n",
    "        else:\n",
    "            act = \"None\"\n",
    "\n",
    "        print(_activation)\n",
    "        pca_raw.append(\n",
    "            {\n",
    "                \"Model\": \"GPT2\",\n",
    "                \"x\": projected[idx, 0].item(),\n",
    "                \"y\": projected[idx, 1].item(),\n",
    "                \"Activated\": act,\n",
    "            }\n",
    "        )\n",
    "\n",
    "    _offset = len(gpt2_resid)\n",
    "    print(\"____\")\n",
    "    for idx in range(num_samples):\n",
    "        _activation = dpo_acts[idx].item()\n",
    "        if _activation > _boundary[1]:\n",
    "            act = f\"High (> {_boundary[1]})\"\n",
    "        elif _activation > _boundary[0]:\n",
    "            act = f\"Low (> {_boundary[0]})\"\n",
    "        else:\n",
    "            act = \"None\"\n",
    "        print(_activation)\n",
    "        pca_raw.append(\n",
    "            {\n",
    "                \"Model\": \"DPO\",\n",
    "                \"x\": projected[_offset + idx, 0].item(),\n",
    "                \"y\": projected[_offset + idx, 1].item(),\n",
    "                \"Activated\": act,\n",
    "            }\n",
    "        )\n",
    "\n",
    "    pca_data = pd.DataFrame(pca_raw)\n",
    "    sns.set_theme(context=\"paper\", style=\"ticks\", rc={\"lines.linewidth\": 1})\n",
    "\n",
    "    ax = fig.add_subplot(gs[0, vec_idx - 1])\n",
    "\n",
    "    legend = None\n",
    "    if vec_idx == 1:\n",
    "        legend = \"full\"\n",
    "\n",
    "    sns.scatterplot(\n",
    "        pca_data,\n",
    "        x=\"x\",\n",
    "        y=\"y\",\n",
    "        hue=\"Activated\",\n",
    "        palette={\n",
    "            f\"High (> {_boundary[1]})\": \"red\",\n",
    "            f\"Low (> {_boundary[0]})\": \"orange\",\n",
    "            \"None\": \"green\",\n",
    "        },\n",
    "        hue_order=[\n",
    "            f\"High (> {_boundary[1]})\",\n",
    "            f\"Low (> {_boundary[0]})\",\n",
    "            \"None\",\n",
    "        ],\n",
    "        style=\"Model\",\n",
    "        markers={\"GPT2\": \"o\", \"DPO\": \"^\"},\n",
    "        s=60,\n",
    "        legend=legend,\n",
    "        ax=ax,\n",
    "    )\n",
    "\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.xaxis.label.set_text(\"Shift Component\")\n",
    "    ax.yaxis.label.set_text(\"Principle Component\")\n",
    "    ax.xaxis.label.set_visible(True)\n",
    "    ax.yaxis.label.set_visible(True)\n",
    "\n",
    "    _offset = len(gpt2_resid)\n",
    "    for idx in range(num_samples):\n",
    "        gpt2_x = projected[idx, 0].item()\n",
    "        gpt2_y = projected[idx, 1].item()\n",
    "        dpo_x = projected[_offset + idx, 0].item()\n",
    "        dpo_y = projected[_offset + idx, 1].item()\n",
    "        ax.plot(\n",
    "            [gpt2_x, dpo_x], [gpt2_y, dpo_y], color=\"black\", ls=\":\", zorder=0\n",
    "        )\n",
    "\n",
    "plt.savefig(f\"pca_layer_appx.pdf\", bbox_inches=\"tight\", dpi=1200)"
   ]
  }
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